The present study examined disclosure and use of privacy settings in online social networking profiles as a function of the media context (i.e., online versus hard copy (paper and pencil) FacebookTM profiles). Gender ...The present study examined disclosure and use of privacy settings in online social networking profiles as a function of the media context (i.e., online versus hard copy (paper and pencil) FacebookTM profiles). Gender was also examined. Overall, participants disclosed more information when constructing a profile for another person when using a hard copy paper and pencil format than an online context. Gender differences were not uniform across media contexts, however, in contrast to traditional disclosure theory, females censored their disclosures more so than males but only for some topics. Only 20% of the sample increased their use of privacy settings. Consistent with patterns of disclosure, descriptive comparison suggests that more settings were employed in the paper and pencil than online context and more privacy settings were employed by females.展开更多
The user-generated social media messages usually contain considerable multimodal content.Such messages are usually short and lack explicit sentiment words.However,we can understand the sentiment associated with such m...The user-generated social media messages usually contain considerable multimodal content.Such messages are usually short and lack explicit sentiment words.However,we can understand the sentiment associated with such messages by analyzing the context,which is essential to improve the sentiment analysis performance.Unfortunately,majority of the existing studies consider the impact of contextual information based on a single data model.In this study,we propose a novel model for performing context-aware user sentiment analysis.This model involves the semantic correlation of different modalities and the effects of tweet context information.Based on our experimental results obtained using the Twitter dataset,our approach is observed to outperform the other existing methods in analysing user sentiment.展开更多
文摘The present study examined disclosure and use of privacy settings in online social networking profiles as a function of the media context (i.e., online versus hard copy (paper and pencil) FacebookTM profiles). Gender was also examined. Overall, participants disclosed more information when constructing a profile for another person when using a hard copy paper and pencil format than an online context. Gender differences were not uniform across media contexts, however, in contrast to traditional disclosure theory, females censored their disclosures more so than males but only for some topics. Only 20% of the sample increased their use of privacy settings. Consistent with patterns of disclosure, descriptive comparison suggests that more settings were employed in the paper and pencil than online context and more privacy settings were employed by females.
基金supported by the National Key R&D Program of China(No.2017YFB1003000)the National Natural Science Foundation of China(Nos.61972087and 61772133)+4 种基金the National Social Science Foundation of China(No.19@ZH014)Jiangsu Provincial Key Project(No.BE2018706)the Natural Science Foundation of Jiangsu Province(No.SBK2019022870)Jiangsu Provincial Key Laboratory of Network and Information Security(No.BM2003201)Key Laboratory of Computer Network and Information Integration of Ministry of Education of China(No.93K-9).
文摘The user-generated social media messages usually contain considerable multimodal content.Such messages are usually short and lack explicit sentiment words.However,we can understand the sentiment associated with such messages by analyzing the context,which is essential to improve the sentiment analysis performance.Unfortunately,majority of the existing studies consider the impact of contextual information based on a single data model.In this study,we propose a novel model for performing context-aware user sentiment analysis.This model involves the semantic correlation of different modalities and the effects of tweet context information.Based on our experimental results obtained using the Twitter dataset,our approach is observed to outperform the other existing methods in analysing user sentiment.
